1. Field of the Invention
The present invention relates to an information processing apparatus and method, a recording medium, and a program. In particular, the present invention relates to an information processing apparatus and method, a recording medium, and a program for enabling image signals to be generated with higher prediction accuracy. The present invention also relates to an information processing apparatus and method, a recording medium, and a program for enabling higher-quality information to be obtained in a shorter period of time. The present invention further relates to an information processing apparatus and method, a recording medium, and a program for enabling image signals to be generated more easily with higher prediction accuracy.
2. Description of the Related Art
The assignee has proposed in Japanese Registered Patent Publication No. 3321915 a method of classification adaptive processing for converting a standard-definition (resolution) television signal (SD signal) to a high-definition (resolution) image signal (HD signal). The principle of classification adaptive processing for generating a HD signal from a SD signal will now be described with reference to FIGS. 1 to 4.
FIG. 1 depicts an example structure of an information processing apparatus 1 based on the known classification adaptive processing. In this example structure, an input SD signal is supplied to an area extracting section 11 and an area extracting section 15. The area extracting section 11 extracts, as class taps, predetermined pixels at a predetermined location on a preset frame of the input SD signal, and supplies them to an ADRC processing section 12. The ADRC processing section 12 applies 1-bit ADRC (Adaptive Dynamic Range Coding) processing to the class taps supplied by the area extracting section 11, and then supplies the obtained 1-bit ADRC code to a class-code determining section 13.
The class-code determining section 13 determines a class code based on the input 1-bit ADRC code and supplies it to a prediction coefficient storing section 14. The relationship between the 1-bit ADRC code and the class code is preset. The prediction coefficient storing section 14 pre-stores prediction coefficients corresponding to the class code, and outputs the prediction coefficients corresponding to the input class code to a prediction calculating section 16.
The area extracting section 15 extracts the pixels in a preset area as prediction taps from the input SD signal, and supplies them to the prediction calculating section 16. The prediction calculating section 16 generates a HD signal by applying a linear simple expression using the prediction coefficients supplied by the prediction coefficient storing section 14 to the prediction taps supplied by the area extracting section 15.
HD signal generation processing by the information processing apparatus 1 in FIG. 1 will now be described with reference to the flowchart in FIG. 2. First in step S1, the area extracting section 11 selects one pixel of interest to be processed from the input SD signal. In step S2, the area extracting section 11 extracts class taps corresponding to the pixel of interest. Which pixels are to be set as class taps in relation to the specified pixel of interest are predetermined.
In step S3, the ADRC processing section 12 applies 1-bit ADRC processing to the class taps extracted by the area extracting section 11. In step S4, the class-code determining section 13 determines a class code based on the 1-bit ADRC code generated in step S3 by the ADRC processing section 12.
In step 5, the area extracting section 15 extracts prediction taps from the input SD signal. The locations of the prediction taps corresponding to the pixel of interest are also preset, and the area extracting section 15 extracts the prediction taps corresponding to the pixel of interest selected in step S1 and supplies them to the prediction calculating section 16. In step S6, the prediction calculating section 16 reads prediction coefficients. More specifically, the prediction coefficient storing section 14 reads prediction coefficients stored at the address corresponding to the class code and outputs them to the prediction calculating section 16. The prediction calculating section 16 reads out the prediction coefficients.
In step S7, the prediction calculating section 16 carries out prediction calculation. More specifically, the prediction calculating section 16 applies the prediction coefficients read from the prediction coefficient storing section 14 to the prediction taps supplied by the area extracting section 15, based on a predetermined linear simple expression, to generate a HD signal. In step S8, the prediction calculating section 16 outputs the HD signal predictively generated through the processing in step S7.
In step S9, the area extracting section 11 determines whether the processing of all pixels has been completed. If there still remains a pixel which has not been processed, the flow returns to step S1 to repeat the same processing. If it is determined in step S9 that the processing of all pixels has been completed, the processing of generating a HD signal from a SD signal ends.
FIG. 3 depicts an example structure of an information processing apparatus 31 for producing, through training, prediction coefficients stored in the prediction coefficient storing section 14. In this information processing apparatus 31, a two-dimensional decimation filter 41 generates a SD signal as a trainee signal from an input HD signal as a trainer image and supplies it to an area extracting section 42 and an area extracting section 45. The area extracting section 42 extracts class taps from the SD signal and supplies them to an ADRC processing section 43. The area extracted by the area extracting section 42 (positional relationships of the class taps with the pixel of interest) is the same as in the area extracting section 11 shown in FIG. 1.
The ADRC processing section 43 applies 1-bit ADRC processing to the class taps supplied by the area extracting section 42 and outputs the ADRC code to a class-code determining section 44.
The class-code determining section 44 determines a class code based on the input ADRC code and outputs it to a normal equation generating section 46. The correspondence between the ADRC code and the class code in the class-code determining section 44 is the same as in the class-code determining section 13 shown in FIG. 1.
The area extracting section 45 extracts prediction taps from the SD signal supplied by the two-dimensional decimation filter 41 and supplies them to a normal equation generating section 46. The prediction area extracted by the area extracting section 45 (positional relationships of the prediction taps with the pixel of interest) is the same as in the area extracting section 15 shown in FIG. 1.
The normal equation generating section 46 generates normal equations including linear simple expressions defining the relationship between the SD signal and the HD signal for each class (class code), and supplies them to a prediction coefficient determining section 47. The prediction coefficient determining section 47 determines prediction coefficients by solving the normal equations supplied by the normal equation generating section 46 through, for example, the least squares method and supplies them to a prediction coefficient storing section 48. The prediction coefficient storing section 48 stores the prediction coefficients supplied by the prediction coefficient determining section 47.
The training processing by the information processing apparatus 31 will now be described with reference to the flowchart shown in FIG. 4. In step S21, the two-dimensional decimation filter 41 generates a SD signal as a trainee image by decimating every other pixel of the input HD signal horizontally and vertically. In step S22, the area extracting section 42 extracts class taps from the SD signal supplied by the two-dimensional decimation filter 41. In step S23, the ADRC processing section 43 applies 1-bit ADRC processing to the class taps supplied by the area extracting section 42. In step S24, the class-code determining section 44 determines a class code based on the ADRC code supplied by the ADRC processing section 43.
On the other hand, in step S25 the area extracting section 45 extracts prediction taps from the SD signal supplied by the two-dimensional decimation filter 41 and outputs them to the normal equation generating section 46. In step S26, the normal equation generating section 46 generates normal equations including linear simple expressions defining the relationships between a HD signal, functioning as a trainer image, and prediction taps (SD signal), functioning as a trainee image, for each class code supplied by the class-code determining section 44. In step S27, the prediction coefficient determining section 47 determines prediction coefficients by solving the normal equations generated by the normal equation generating section 46 through, for example, the least squares method. In step S28, the prediction coefficient storing section 48 stores the prediction coefficients supplied by the prediction coefficient determining section 47.
In this manner, the prediction coefficients stored in the prediction coefficient storing section 48 are used in the prediction coefficient storing section 14 shown in FIG. 1.
As described above, a prediction coefficient set is generated through training based on a prepared HD image signal and a SD image signal generated from the HD image signal. This training is carried out based on many types of HD image signals. As a result, a prediction coefficient set based on the relationships between many types of HD image signals and SD image signals is obtained.
Applying this prediction coefficient set to a received SD image signal enables a HD image signal not actually received to be predicted and generated. The prediction coefficient set thus obtained is based on a statistical property that is most likely to generate a signal as similar to an actual HD signal as possible in response to an input SD signal. As a result, when a standard SD image signal is input, a HD image signal with high accuracy on the average for each class can be predicted and generated.
A sufficient number of HD signals are required during training to acquire prediction coefficients through this classification adaptive processing. However, some classes may not experience a sufficient amount of training depending on training materials. A class with a small amount of training cannot generate appropriate coefficients. If a HD signal is generated from a SD signal with prediction coefficients produced in this manner, it is difficult to generate a HD signal with satisfactorily enhanced image quality.
To overcome this problem, the assignee has disclosed, in Japanese Unexamined Patent Application Publication No. 2000-78536, a method for seemingly increasing the number of training materials by intentionally adding random numbers (noise) during training.
With the known classification adaptive processing, in which a predetermined number of classes are prepared and prediction coefficients are generated based on the prepared classes only, images with satisfactorily high quality are not generated in some cases.
The method of adding random numbers does not always ensure a sufficient amount of training depending on classes because the number of classes is reduced and fixed, resulting in a failure to generate images with satisfactorily high quality.
There is another problem that a HD image signal used to generate prediction coefficients through training differs from an actually predicted HD image signal. This makes it difficult to ensure accurate prediction calculation processing.
A sufficient number of classes are required to overcome this problem. Unfortunately, the number of classes is limited, and if an appropriate class is not available during training with HD image signals, a class which is not appropriate has to be used. This often prevents accurate prediction processing.
Moreover, the known method requires a HD image signal to generate a prediction coefficient set. As a result, the processing of generating a prediction coefficient set through training must be carried out at a different time or place from the processing of generating a HD image signal from a SD image signal by the use of the generated prediction coefficient set. In short, the known method is problematic in that real-time processing from generation of coefficients to generation of HD image signals is difficult.